Whole-Genome Prediction of Type-2 Diabetes Susceptibility in Various Populations

描述(由申请人提供)：申请人的职业目标是成为统计遗传学领域的一名富有成效的独立研究员，特别是在基于基因组的2型糖尿病(T2D)风险预测领域。为了实现这一目标，申请者提出了一项职业发展计划，其中包括应用于数量遗传学、分类和病例对照数据分析的统计学学习、应用于高维遗传数据的信息学以及T2D的生物学和遗传学方面的实践和教学培训。一组高成就和多样化的调查人员将监督申请人的职业发展，他们拥有成功指导的记录。这个项目的研究部分旨在提高我们使用遗传信息预测个人患T2D风险的能力。来自DBGaP(表型和基因类型数据库)等来源的公开遗传和表型数据将用于开发和测试在三个种族/民族中预测T2D风险的各种模型。该项目将利用新开发的统计方法，这些方法能够同时纳入来自数万个遗传标记的信息，这比目前通常只考虑不到100个标记的方法是一个重大进步。这项研究的目的是：1)在不同的人群中检验这一假设，即个性化的T2D全基因组预测(以及性别、年龄和BMI的标准协变量)将提供比现有基于遗传学的预测模型更大的改进，并将提供与基于家族史的预测相同或更高的准确性；2)引入额外的遗传标记以确定预测是否可以改进，并确定对预测T2D最有用的标记子集；3)通过包括BMI和基因类型的交互作用作为预测因子，开发T2D风险的预测模型。该项目将极大地提高我们在不同人群中预测个人对T2D易感性的能力，导致更早和有针对性的预防策略，将增加我们对T2D遗传基础的理解，并将为Klimentidis博士作为独立科学家的发展提供关键培训。

DESCRIPTION (provided by applicant): The applicant's career goal is to become a productive independent investigator in the area of statistical genetics, particularly in the area of genomic-based prediction of type-2 diabetes (T2D) risk. To meet this goal, the applicant proposes a career development plan that includes hands- on and didactic training in statistical learning as applied to quantitative genetics, categorical and case-control data analysis, informatics as applied to high dimensional genetic data, and the biology and genetics of T2D. A highly accomplished and diverse set of investigators with proven track records of successful mentoring will oversee the applicant's career development. The research component of this project seeks to improve our ability to use genetic information to predict an individual's risk of developing T2D Publically available genetic and phenotypic data from sources such as dbGaP (The database of Phenotypes and Genotypes) will be used to develop and test various models for prediction of T2D risk among three racial/ethnic groups. This project will capitalize on newly developed statistical methods that are able to incorporate information from tens of thousands of genetic markers at once, which represent a major advance over current methods that typically take fewer than 100 markers into account. The aims of the study are: 1) To test the hypothesis, in different populations, that individualized whole-genome prediction of T2D (along with standard covariates of sex, age, and BMI) will offer major improvements over current genetics-based prediction models, and will offer equal or greater accuracy than prediction based on family history; 2) To impute additional genetic markers to determine whether prediction can be improved, and to identify the subset of markers that is most useful in predicting T2D; 3) To develop prediction models for T2D risk given a certain body mass index (BMI), by including as predictors the interaction of BMI and genotypes. This project will greatly enhance our ability to predict an individual's susceptibility to T2D within various populations, leading to earlier and targeted prevention strategies, will increase our understanding of the genetic basis of T2D, and will provide critical training for Dr. Klimentidis' development as an independent scientist.